AI-enabled Predictive Maintenance of Wind Generators
Bibliographic record
Abstract
Recent policies have led to the development and deployment of renewable energy sources, introducing new operational challenges. Among those, is the problem of extended downtime of RES, such as wind turbines. In this context, it is important to minimize the downtime of renewable assets by an optimal maintenance strategy via early fault detection. As a Supervisory Control and Data Acquisition (SCADA) system is an integrated part of any production facility and collects large amounts of data, machine learning techniques can be used to detect the underlying failure patterns and notify customers of the abnormal behaviour. Thus, in this work, a novel framework based on machine learning algorithms for fault prediction of wind farm generators is presented for an actual customer. The proposed fault prognosis methodology is tested and validated using historical data from a wind farm in Summerside, Prince Edward Island, Canada, and models are evaluated based on appropriate metrics. The results demonstrate the ability of the proposed methodology to predict wind generator failures with a precision as high as 83%, and the viability of the proposed methodology for optimizing predictive maintenance strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".